2014
DOI: 10.1109/tnsre.2013.2290870
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Spatial Filtering Based on Canonical Correlation Analysis for Classification of Evoked or Event-Related Potentials in EEG Data

Abstract: Classification of evoked or event-related potentials is an important prerequisite for many types of brain-computer interfaces (BCIs). To increase classification accuracy, spatial filters are used to improve the signal-to-noise ratio of the brain signals and thereby facilitate the detection and classification of evoked or event-related potentials. While canonical correlation analysis (CCA) has previously been used to construct spatial filters that increase classification accuracy for BCIs based on visual evoked… Show more

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Cited by 75 publications
(48 citation statements)
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“…In contrast, down-sampled data does not provide advantage in terms of accuracy. Rather, a sampling rate between 20 and 40 Hz, as typically used in P300 detection algorithms using EEG [5,6,22,23], yielded slightly lower recognition rates compared with sampling the data at higher rates.…”
Section: Discussionmentioning
confidence: 98%
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“…In contrast, down-sampled data does not provide advantage in terms of accuracy. Rather, a sampling rate between 20 and 40 Hz, as typically used in P300 detection algorithms using EEG [5,6,22,23], yielded slightly lower recognition rates compared with sampling the data at higher rates.…”
Section: Discussionmentioning
confidence: 98%
“…The highest accuracies were achieved equally with two different sets of model functions. The first set was based on empirical estimates constructing reference signals according to [6] and the second set was designed to compose the temporal evolution of the signal. Using the latter method, CCA constructs a spatial filter matrix and concurrently determines brain dynamics.…”
Section: Discussionmentioning
confidence: 99%
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